from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	Optional[int]:







 '''simple docstring'''
 for param, grad_param in zip(model_a.parameters()							,					model_b.parameters()       ):
  if not param.requires_grad:
   continue
  if not did_step:
   # Grads should not be in sync
   assert (
       torch.allclose(param.grad							,					grad_param.grad       ) is False
   ), f"""Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})"""
  else:
   # Grads should be in sync
   assert (
       torch.allclose(param.grad							,					grad_param.grad       ) is True
   ), f"""Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})"""





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_=True       )		->	Any:







 '''simple docstring'''
 model.train()
 UpperCamelCase			  =      model(UpperCamelCase_       )
 UpperCamelCase			  =      F.mse_loss(UpperCamelCase_							,					target.to(output.device       )       )
 if not do_backward:
  loss /= accelerator.gradient_accumulation_steps
  loss.backward()
 else:
  accelerator.backward(UpperCamelCase_       )





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_=False       )		->	List[str]:







 '''simple docstring'''
 set_seed(42       )
 UpperCamelCase			  =      RegressionModel()
 UpperCamelCase			  =      deepcopy(UpperCamelCase_       )
 UpperCamelCase			  =      RegressionDataset(length=80       )
 UpperCamelCase			  =      DataLoader(UpperCamelCase_							,					batch_size=16       )
 model.to(accelerator.device       )
 if sched:
  UpperCamelCase			  =      AdamW(params=model.parameters()							,					lr=1E-3       )
  UpperCamelCase			  =      AdamW(params=ddp_model.parameters()							,					lr=1E-3       )
  UpperCamelCase			  =      LambdaLR(UpperCamelCase_							,					lr_lambda=lambda UpperCamelCase_       : epoch**0.6_5       )
  UpperCamelCase			  =      LambdaLR(UpperCamelCase_							,					lr_lambda=lambda UpperCamelCase_       : epoch**0.6_5       )
 # Make a copy of `model`
 if sched:
  UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      accelerator.prepare(UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )
 else:
  UpperCamelCase  ,     UpperCamelCase			  =      accelerator.prepare(UpperCamelCase_							,					UpperCamelCase_       )
 if sched:
  return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
 return model, ddp_model, dataloader





def 			lowercase(	UpperCamelCase_       )		->	int:







 '''simple docstring'''
 # Test when on a single CPU or GPU that the context manager does nothing
 UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      get_training_setup(UpperCamelCase_       )
 # Use a single batch
 UpperCamelCase  ,     UpperCamelCase			  =      next(iter(UpperCamelCase_       )       ).values()
 for iteration in range(3       ):
  # Gather the distributed inputs and targs for the base model
  UpperCamelCase  ,     UpperCamelCase			  =      accelerator.gather((ddp_input, ddp_target)       )
  UpperCamelCase  ,     UpperCamelCase			  =      input.to(accelerator.device       ), target.to(accelerator.device       )
  # Perform our initial ground truth step in non "DDP"
  step_model(UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )
  # Do "gradient accumulation" (noop)
  if iteration % 2 == 0:
   # Accumulate grads locally
   with accelerator.no_sync(UpperCamelCase_       ):
    step_model(UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )
  else:
   # Sync grads
   step_model(UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )

  # Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
  check_model_parameters(UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )
  for param, ddp_param in zip(model.parameters()							,					ddp_model.parameters()       ):
   if not param.requires_grad:
    continue
   assert torch.allclose(
       param.grad							,					ddp_param.grad       ), f"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""

  # Shuffle ddp_input on each iteration
  torch.manual_seed(1337 + iteration       )
  UpperCamelCase			  =      ddp_input[torch.randperm(len(UpperCamelCase_       )       )]





def 			lowercase(	UpperCamelCase_       )		->	Union[str, Any]:







 '''simple docstring'''
 # Test on distributed setup that context manager behaves properly
 UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      get_training_setup(UpperCamelCase_       )
 # Use a single batch
 UpperCamelCase  ,     UpperCamelCase			  =      next(iter(UpperCamelCase_       )       ).values()
 for iteration in range(3       ):
  # Gather the distributed inputs and targs for the base model
  UpperCamelCase  ,     UpperCamelCase			  =      accelerator.gather((ddp_input, ddp_target)       )
  UpperCamelCase  ,     UpperCamelCase			  =      input.to(accelerator.device       ), target.to(accelerator.device       )
  # Perform our initial ground truth step in non "DDP"
  step_model(UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )
  # Do "gradient accumulation" (noop)
  if iteration % 2 == 0:
   # Accumulate grads locally
   with accelerator.no_sync(UpperCamelCase_       ):
    step_model(UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )
  else:
   # Sync grads
   step_model(UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )

  # DDP model and model should only be in sync when not (iteration % 2 == 0)
  for param, ddp_param in zip(model.parameters()							,					ddp_model.parameters()       ):
   if not param.requires_grad:
    continue
   if iteration % 2 == 0:
    # Grads should not be in sync
    assert (
        torch.allclose(param.grad							,					ddp_param.grad       ) is False
    ), f"""Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""
   else:
    # Grads should be in sync
    assert (
        torch.allclose(param.grad							,					ddp_param.grad       ) is True
    ), f"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""

        # Shuffle ddp_input on each iteration
  torch.manual_seed(1337 + iteration       )
  UpperCamelCase			  =      ddp_input[torch.randperm(len(UpperCamelCase_       )       )]





def 			lowercase(	UpperCamelCase_=False							,					UpperCamelCase_=False       )		->	Tuple:







 '''simple docstring'''
 UpperCamelCase			  =      Accelerator(
     split_batches=UpperCamelCase_							,					dispatch_batches=UpperCamelCase_							,					gradient_accumulation_steps=2       )
 # Test that context manager behaves properly
 UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      get_training_setup(UpperCamelCase_       )
 for iteration, batch in enumerate(UpperCamelCase_       ):
  UpperCamelCase  ,     UpperCamelCase			  =      batch.values()
  # Gather the distributed inputs and targs for the base model
  UpperCamelCase  ,     UpperCamelCase			  =      accelerator.gather((ddp_input, ddp_target)       )
  UpperCamelCase  ,     UpperCamelCase			  =      input.to(accelerator.device       ), target.to(accelerator.device       )
  # Perform our initial ground truth step in non "DDP"
  step_model(UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )
  # Do "gradient accumulation" (noop)
  with accelerator.accumulate(UpperCamelCase_       ):
   step_model(UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )

  # DDP model and model should only be in sync when not (iteration % 2 == 0)
  for param, ddp_param in zip(model.parameters()							,					ddp_model.parameters()       ):
   if not param.requires_grad:
    continue
   if ((iteration + 1) % 2 == 0) or (iteration == len(UpperCamelCase_       ) - 1):
    # Grads should be in sync
    assert (
        torch.allclose(param.grad							,					ddp_param.grad       ) is True
    ), f"""Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
   else:
    # Grads should not be in sync
    assert (
        torch.allclose(param.grad							,					ddp_param.grad       ) is False
    ), f"""Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""

        # Shuffle ddp_input on each iteration
  torch.manual_seed(1337 + iteration       )
  UpperCamelCase			  =      ddp_input[torch.randperm(len(UpperCamelCase_       )       )]
 GradientState._reset_state()





def 			lowercase(	UpperCamelCase_=False							,					UpperCamelCase_=False       )		->	List[Any]:







 '''simple docstring'''
 UpperCamelCase			  =      Accelerator(
     split_batches=UpperCamelCase_							,					dispatch_batches=UpperCamelCase_							,					gradient_accumulation_steps=2       )
 # Test that context manager behaves properly
 UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      get_training_setup(UpperCamelCase_							,					UpperCamelCase_       )
 for iteration, batch in enumerate(UpperCamelCase_       ):
  UpperCamelCase  ,     UpperCamelCase			  =      batch.values()
  # Gather the distributed inputs and targs for the base model
  UpperCamelCase  ,     UpperCamelCase			  =      accelerator.gather((ddp_input, ddp_target)       )
  UpperCamelCase  ,     UpperCamelCase			  =      input.to(accelerator.device       ), target.to(accelerator.device       )
  # Perform our initial ground truth step in non "DDP"
  model.train()
  ddp_model.train()
  step_model(UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )
  opt.step()

  if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(UpperCamelCase_       )):
   if split_batches:
    sched.step()
   else:
    for _ in range(accelerator.num_processes       ):
     sched.step()
  opt.zero_grad()
  # Perform gradient accumulation under wrapper
  with accelerator.accumulate(UpperCamelCase_       ):
   step_model(UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )
   ddp_opt.step()
   ddp_sched.step()
   ddp_opt.zero_grad()

  # Learning rates should be the same
  assert (
      opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
  ), f"""Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]["lr"]}\nDDP opt: {ddp_opt.param_groups[0]["lr"]}\n"""
  UpperCamelCase			  =      (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(UpperCamelCase_       ))
  if accelerator.num_processes > 1:
   check_model_parameters(UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )
  # Shuffle ddp_input on each iteration
  torch.manual_seed(1337 + iteration       )
 GradientState._reset_state()





def 			lowercase(	)		->	List[Any]:







 '''simple docstring'''
 UpperCamelCase			  =      Accelerator()

 UpperCamelCase			  =      RegressionDataset(length=80       )
 UpperCamelCase			  =      DataLoader(UpperCamelCase_							,					batch_size=16       )
 UpperCamelCase			  =      RegressionDataset(length=96       )
 UpperCamelCase			  =      DataLoader(UpperCamelCase_							,					batch_size=16       )
 UpperCamelCase  ,     UpperCamelCase			  =      accelerator.prepare(UpperCamelCase_							,					UpperCamelCase_       )
 assert accelerator.gradient_state.active_dataloader is None
 for iteration, _ in enumerate(UpperCamelCase_       ):
  assert id(accelerator.gradient_state.active_dataloader       ) == id(UpperCamelCase_       )
  if iteration < len(UpperCamelCase_       ) - 1:
   assert not accelerator.gradient_state.end_of_dataloader
   if iteration == 1:
    for batch_num, _ in enumerate(UpperCamelCase_       ):
     assert id(accelerator.gradient_state.active_dataloader       ) == id(UpperCamelCase_       )
     if batch_num < len(UpperCamelCase_       ) - 1:
      assert not accelerator.gradient_state.end_of_dataloader
     else:
      assert accelerator.gradient_state.end_of_dataloader
  else:
   assert accelerator.gradient_state.end_of_dataloader
 assert accelerator.gradient_state.active_dataloader is None





def 			lowercase(	)		->	Union[str, Any]:







 '''simple docstring'''
 UpperCamelCase			  =      Accelerator()
 UpperCamelCase			  =      accelerator.state
 if state.local_process_index == 0:
  print("""**Test `accumulate` gradient accumulation with dataloader break**"""       )
 test_dataloader_break()
 if state.distributed_type == DistributedType.NO:
  if state.local_process_index == 0:
   print("""**Test NOOP `no_sync` context manager**"""       )
  test_noop_sync(UpperCamelCase_       )
 if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
  if state.local_process_index == 0:
   print("""**Test Distributed `no_sync` context manager**"""       )
  test_distributed_sync(UpperCamelCase_       )
 if state.distributed_type == DistributedType.MULTI_GPU:
  for split_batch in [True, False]:
   for dispatch_batches in [True, False]:
    if state.local_process_index == 0:
     print(
         """**Test `accumulate` gradient accumulation, """							,					f"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**"""							,					)
    test_gradient_accumulation(UpperCamelCase_							,					UpperCamelCase_       )

    # Currently will break on torch 2.0 +, need to investigate why
 if is_torch_version("""<"""							,					"""2.0"""       ) or state.distributed_type == DistributedType.NO:
  if state.local_process_index == 0:
   print(
       """**Test `accumulate` gradient accumulation with optimizer and scheduler, """							,					"""`split_batches=False`, `dispatch_batches=False`**"""							,					)
  test_gradient_accumulation_with_opt_and_scheduler()
  if state.distributed_type == DistributedType.MULTI_GPU:
   for split_batch in [True, False]:
    for dispatch_batches in [True, False]:
     if not split_batch and not dispatch_batches:
      continue
     if state.local_process_index == 0:
      print(
          """**Test `accumulate` gradient accumulation with optimizer and scheduler, """							,					f"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**"""							,					)
     test_gradient_accumulation_with_opt_and_scheduler(UpperCamelCase_							,					UpperCamelCase_       )





def 			lowercase(	UpperCamelCase_       )		->	int:







 '''simple docstring'''
 # For xla_spawn (TPUs)
 main()


if __name__ == "__main__":
     main()

import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase  ,     __lowerCAmelCase		):







   @register_to_config
   def __init__(     self						:						List[Any]   ,   lowerCamelCase_						:						int = 128   ,   lowerCamelCase_						:						int = 256   ,   lowerCamelCase_						:						float = 2_0_0_0.0   ,   lowerCamelCase_						:						int = 768   ,   lowerCamelCase_						:						int = 12   ,   lowerCamelCase_						:						int = 12   ,   lowerCamelCase_						:						int = 64   ,   lowerCamelCase_						:						int = 2048   ,   lowerCamelCase_						:						float = 0.1   ,   ):





    """simple docstring"""


    super().__init__()

    UpperCamelCase			  =      nn.Sequential(
        nn.Linear(lowerCamelCase_   ,   d_model * 4   ,   bias=lowerCamelCase_				)   ,   nn.SiLU()   ,   nn.Linear(d_model * 4   ,   d_model * 4   ,   bias=lowerCamelCase_				)   ,   nn.SiLU()   ,   )

    UpperCamelCase			  =      nn.Embedding(lowerCamelCase_   ,   lowerCamelCase_				)
    UpperCamelCase			  =      False

    UpperCamelCase			  =      nn.Linear(lowerCamelCase_   ,   lowerCamelCase_   ,   bias=lowerCamelCase_				)

    UpperCamelCase			  =      nn.Dropout(p=lowerCamelCase_				)

    UpperCamelCase			  =      nn.ModuleList()
    for lyr_num in range(lowerCamelCase_				):
     # FiLM conditional T5 decoder
     UpperCamelCase			  =      DecoderLayer(d_model=lowerCamelCase_   ,   d_kv=lowerCamelCase_   ,   num_heads=lowerCamelCase_   ,   d_ff=lowerCamelCase_   ,   dropout_rate=lowerCamelCase_				)
     self.decoders.append(lowerCamelCase_				)

    UpperCamelCase			  =      TaLayerNorm(lowerCamelCase_				)

    UpperCamelCase			  =      nn.Dropout(p=lowerCamelCase_				)
    UpperCamelCase			  =      nn.Linear(lowerCamelCase_   ,   lowerCamelCase_   ,   bias=lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Union[str, Any]   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						List[str]				):





    """simple docstring"""


    UpperCamelCase			  =      torch.mul(query_input.unsqueeze(-1				)   ,   key_input.unsqueeze(-2				)				)
    return mask.unsqueeze(-3				)







   def 						lowerCamelCase_	(     self						:						List[Any]   ,   lowerCamelCase_						:						List[str]   ,   lowerCamelCase_						:						Optional[int]   ,   lowerCamelCase_						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      decoder_input_tokens.shape
    assert decoder_noise_time.shape == (batch,)

    # decoder_noise_time is in [0, 1), so rescale to expected timing range.
    UpperCamelCase			  =      get_timestep_embedding(
        decoder_noise_time * self.config.max_decoder_noise_time   ,   embedding_dim=self.config.d_model   ,   max_period=self.config.max_decoder_noise_time   ,   ).to(dtype=self.dtype				)

    UpperCamelCase			  =      self.conditioning_emb(lowerCamelCase_				).unsqueeze(1				)

    assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

    UpperCamelCase			  =      decoder_input_tokens.shape[1]

    # If we want to use relative positions for audio context, we can just offset
    # this sequence by the length of encodings_and_masks.
    UpperCamelCase			  =      torch.broadcast_to(
        torch.arange(lowerCamelCase_   ,   device=decoder_input_tokens.device				)   ,   (batch, seq_length)   ,   )

    UpperCamelCase			  =      self.position_encoding(lowerCamelCase_				)

    UpperCamelCase			  =      self.continuous_inputs_projection(lowerCamelCase_				)
    inputs += position_encodings
    UpperCamelCase			  =      self.dropout(lowerCamelCase_				)

    # decoder: No padding present.
    UpperCamelCase			  =      torch.ones(
        decoder_input_tokens.shape[:2]   ,   device=decoder_input_tokens.device   ,   dtype=inputs.dtype				)

    # Translate encoding masks to encoder-decoder masks.
    UpperCamelCase			  =      [(x, self.encoder_decoder_mask(lowerCamelCase_   ,   lowerCamelCase_				)) for x, y in encodings_and_masks]

    # cross attend style: concat encodings
    UpperCamelCase			  =      torch.cat([x[0] for x in encodings_and_encdec_masks]   ,   dim=1				)
    UpperCamelCase			  =      torch.cat([x[1] for x in encodings_and_encdec_masks]   ,   dim=-1				)

    for lyr in self.decoders:
     UpperCamelCase			  =      lyr(
         lowerCamelCase_   ,   conditioning_emb=lowerCamelCase_   ,   encoder_hidden_states=lowerCamelCase_   ,   encoder_attention_mask=lowerCamelCase_   ,   )[0]

    UpperCamelCase			  =      self.decoder_norm(lowerCamelCase_				)
    UpperCamelCase			  =      self.post_dropout(lowerCamelCase_				)

    UpperCamelCase			  =      self.spec_out(lowerCamelCase_				)
    return spec_out





class SCREAMING_SNAKE_CASE_		(      nn.Module		):







   def __init__(     self						:						List[str]   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						List[str]   ,   lowerCamelCase_						:						List[str]   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						Any=1E-6				):





    """simple docstring"""


    super().__init__()
    UpperCamelCase			  =      nn.ModuleList()

    # cond self attention: layer 0
    self.layer.append(
        TaLayerSelfAttentionCond(d_model=lowerCamelCase_   ,   d_kv=lowerCamelCase_   ,   num_heads=lowerCamelCase_   ,   dropout_rate=lowerCamelCase_				)				)

    # cross attention: layer 1
    self.layer.append(
        TaLayerCrossAttention(
            d_model=lowerCamelCase_   ,   d_kv=lowerCamelCase_   ,   num_heads=lowerCamelCase_   ,   dropout_rate=lowerCamelCase_   ,   layer_norm_epsilon=lowerCamelCase_   ,   )				)

    # Film Cond MLP + dropout: last layer
    self.layer.append(
        TaLayerFFCond(d_model=lowerCamelCase_   ,   d_ff=lowerCamelCase_   ,   dropout_rate=lowerCamelCase_   ,   layer_norm_epsilon=lowerCamelCase_				)				)







   def 						lowerCamelCase_	(     self						:						List[str]   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						List[Any]=None   ,   lowerCamelCase_						:						int=None   ,   lowerCamelCase_						:						Optional[int]=None   ,   lowerCamelCase_						:						Any=None   ,   lowerCamelCase_						:						Union[str, Any]=None   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      self.layer[0](
        lowerCamelCase_   ,   conditioning_emb=lowerCamelCase_   ,   attention_mask=lowerCamelCase_   ,   )

    if encoder_hidden_states is not None:
     UpperCamelCase			  =      torch.where(encoder_attention_mask > 0   ,   0   ,   -1E10				).to(
         encoder_hidden_states.dtype				)

     UpperCamelCase			  =      self.layer[1](
         lowerCamelCase_   ,   key_value_states=lowerCamelCase_   ,   attention_mask=lowerCamelCase_   ,   )

    # Apply Film Conditional Feed Forward layer
    UpperCamelCase			  =      self.layer[-1](lowerCamelCase_   ,   lowerCamelCase_				)

    return (hidden_states,)





class SCREAMING_SNAKE_CASE_		(      nn.Module		):







   def __init__(     self						:						Dict   ,   lowerCamelCase_						:						Any   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						str   ,   lowerCamelCase_						:						List[Any]				):





    """simple docstring"""


    super().__init__()
    UpperCamelCase			  =      TaLayerNorm(lowerCamelCase_				)
    UpperCamelCase			  =      TaFiLMLayer(in_features=d_model * 4   ,   out_features=lowerCamelCase_				)
    UpperCamelCase			  =      Attention(query_dim=lowerCamelCase_   ,   heads=lowerCamelCase_   ,   dim_head=lowerCamelCase_   ,   out_bias=lowerCamelCase_   ,   scale_qk=lowerCamelCase_				)
    UpperCamelCase			  =      nn.Dropout(lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						int   ,   lowerCamelCase_						:						Optional[Any]   ,   lowerCamelCase_						:						Tuple=None   ,   lowerCamelCase_						:						Optional[Any]=None   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      self.layer_norm(lowerCamelCase_				)

    if conditioning_emb is not None:
     UpperCamelCase			  =      self.FiLMLayer(lowerCamelCase_   ,   lowerCamelCase_				)

    # Self-attention block
    UpperCamelCase			  =      self.attention(lowerCamelCase_				)

    UpperCamelCase			  =      hidden_states + self.dropout(lowerCamelCase_				)

    return hidden_states





class SCREAMING_SNAKE_CASE_		(      nn.Module		):







   def __init__(     self						:						Dict   ,   lowerCamelCase_						:						str   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						Optional[Any]   ,   lowerCamelCase_						:						str   ,   lowerCamelCase_						:						Dict				):





    """simple docstring"""


    super().__init__()
    UpperCamelCase			  =      Attention(query_dim=lowerCamelCase_   ,   heads=lowerCamelCase_   ,   dim_head=lowerCamelCase_   ,   out_bias=lowerCamelCase_   ,   scale_qk=lowerCamelCase_				)
    UpperCamelCase			  =      TaLayerNorm(lowerCamelCase_   ,   eps=lowerCamelCase_				)
    UpperCamelCase			  =      nn.Dropout(lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Optional[Any]   ,   lowerCamelCase_						:						List[str]   ,   lowerCamelCase_						:						Dict=None   ,   lowerCamelCase_						:						int=None   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      self.layer_norm(lowerCamelCase_				)
    UpperCamelCase			  =      self.attention(
        lowerCamelCase_   ,   encoder_hidden_states=lowerCamelCase_   ,   attention_mask=attention_mask.squeeze(1				)   ,   )
    UpperCamelCase			  =      hidden_states + self.dropout(lowerCamelCase_				)
    return layer_output





class SCREAMING_SNAKE_CASE_		(      nn.Module		):







   def __init__(     self						:						Union[str, Any]   ,   lowerCamelCase_						:						Any   ,   lowerCamelCase_						:						str   ,   lowerCamelCase_						:						Optional[int]   ,   lowerCamelCase_						:						Dict				):





    """simple docstring"""


    super().__init__()
    UpperCamelCase			  =      TaDenseGatedActDense(d_model=lowerCamelCase_   ,   d_ff=lowerCamelCase_   ,   dropout_rate=lowerCamelCase_				)
    UpperCamelCase			  =      TaFiLMLayer(in_features=d_model * 4   ,   out_features=lowerCamelCase_				)
    UpperCamelCase			  =      TaLayerNorm(lowerCamelCase_   ,   eps=lowerCamelCase_				)
    UpperCamelCase			  =      nn.Dropout(lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						List[Any]   ,   lowerCamelCase_						:						List[Any]   ,   lowerCamelCase_						:						int=None				):





    """simple docstring"""


    UpperCamelCase			  =      self.layer_norm(lowerCamelCase_				)
    if conditioning_emb is not None:
     UpperCamelCase			  =      self.film(lowerCamelCase_   ,   lowerCamelCase_				)

    UpperCamelCase			  =      self.DenseReluDense(lowerCamelCase_				)
    UpperCamelCase			  =      hidden_states + self.dropout(lowerCamelCase_				)
    return hidden_states





class SCREAMING_SNAKE_CASE_		(      nn.Module		):







   def __init__(     self						:						List[Any]   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						List[Any]   ,   lowerCamelCase_						:						List[Any]				):





    """simple docstring"""


    super().__init__()
    UpperCamelCase			  =      nn.Linear(lowerCamelCase_   ,   lowerCamelCase_   ,   bias=lowerCamelCase_				)
    UpperCamelCase			  =      nn.Linear(lowerCamelCase_   ,   lowerCamelCase_   ,   bias=lowerCamelCase_				)
    UpperCamelCase			  =      nn.Linear(lowerCamelCase_   ,   lowerCamelCase_   ,   bias=lowerCamelCase_				)
    UpperCamelCase			  =      nn.Dropout(lowerCamelCase_				)
    UpperCamelCase			  =      NewGELUActivation()







   def 						lowerCamelCase_	(     self						:						Union[str, Any]   ,   lowerCamelCase_						:						Optional[int]				):





    """simple docstring"""


    UpperCamelCase			  =      self.act(self.wi_a(lowerCamelCase_				)				)
    UpperCamelCase			  =      self.wi_a(lowerCamelCase_				)
    UpperCamelCase			  =      hidden_gelu * hidden_linear
    UpperCamelCase			  =      self.dropout(lowerCamelCase_				)

    UpperCamelCase			  =      self.wo(lowerCamelCase_				)
    return hidden_states





class SCREAMING_SNAKE_CASE_		(      nn.Module		):







   def __init__(     self						:						Optional[int]   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						Dict=1E-6				):





    """simple docstring"""


    super().__init__()
    UpperCamelCase			  =      nn.Parameter(torch.ones(lowerCamelCase_				)				)
    UpperCamelCase			  =      eps







   def 						lowerCamelCase_	(     self						:						List[Any]   ,   lowerCamelCase_						:						Any				):





    """simple docstring"""


    UpperCamelCase			  =      hidden_states.to(torch.floataa				).pow(2				).mean(-1   ,   keepdim=lowerCamelCase_				)
    UpperCamelCase			  =      hidden_states * torch.rsqrt(variance + self.variance_epsilon				)

    # convert into half-precision if necessary
    if self.weight.dtype in [torch.floataa, torch.bfloataa]:
     UpperCamelCase			  =      hidden_states.to(self.weight.dtype				)

    return self.weight * hidden_states





class SCREAMING_SNAKE_CASE_		(      nn.Module		):







   def 						lowerCamelCase_	(     self						:						List[Any]   ,   lowerCamelCase_						:						torch.Tensor				):





    """simple docstring"""


    return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi				) * (input + 0.0_4_4_7_1_5 * torch.pow(lowerCamelCase_   ,   3.0				))				))





class SCREAMING_SNAKE_CASE_		(      nn.Module		):







   def __init__(     self						:						Optional[Any]   ,   lowerCamelCase_						:						List[str]   ,   lowerCamelCase_						:						str				):





    """simple docstring"""


    super().__init__()
    UpperCamelCase			  =      nn.Linear(lowerCamelCase_   ,   out_features * 2   ,   bias=lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						List[str]   ,   lowerCamelCase_						:						Optional[Any]   ,   lowerCamelCase_						:						Dict				):





    """simple docstring"""


    UpperCamelCase			  =      self.scale_bias(lowerCamelCase_				)
    UpperCamelCase  ,     UpperCamelCase			  =      torch.chunk(lowerCamelCase_   ,   2   ,   -1				)
    UpperCamelCase			  =      x * (1 + scale) + shift
    return x
